braindecode.augmentation.SensorsXRotation#

class braindecode.augmentation.SensorsXRotation(probability, ordered_ch_names, max_degrees=15, spherical_splines=True, random_state=None)[source]#

Interpolates EEG signals over sensors rotated around the X axis with an angle sampled uniformly between -max_degree and max_degree.

Suggested in [1]

Parameters:

  • probability (float) – Float setting the probability of applying the operation.

  • ordered_ch_names (list) – List of strings representing the channels of the montage considered. Has to be in standard 10-20 style. The order has to be consistent with the order of channels in the input matrices that will be fed to the transform. This channel will be used to compute approximate sensors positions from a standard 10-20 montage.

  • max_degree (float, optional) – Maximum rotation. Rotation angles will be sampled between -max_degree and max_degree. Defaults to 15 degrees.

  • spherical_splines (bool, optional) – Whether to use spherical splines for the interpolation or not. When False, standard scipy.interpolate.Rbf (with quadratic kernel) will be used (as in the original paper). Defaults to True.

  • random_state (int | numpy.random.Generator, optional) – Seed to be used to instantiate numpy random number generator instance. Defaults to None.

References

[1]

Krell, M. M., & Kim, S. K. (2017). Rotational data augmentation for electroencephalographic data. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 471-474).

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